Generating natural language descriptions of images

Patent metadata

Field	Value
Publication number	US-10417557-B2
Application number	US-201715856453-A
Country	US
Kind code	B2
Filing date	Dec 28, 2017
Priority date	Nov 14, 2014
Publication date	Sep 17, 2019
Grant date	Sep 17, 2019

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

First claim

What is claimed is:

1. One or more non-transitory computer-storage media having instructions encoded thereon that, when executed by one or more computers, cause the one or more computers to perform operations comprising:
obtaining an input image;
processing the input image using a first neural network to generate an alternative representation for the input image; and
processing, with a long-short term memory (LSTM) neural network, the alternative representation for the input image to generate an output sequence of words in a target natural language that describes the input image, wherein:
the words in the output sequence are arranged according to an output order, and
processing the alternative representation for the input image comprises, for each position in the output order after an initial position:
(i) identifying a word that was selected for the output sequence at a preceding position in the output order that precedes the current position;
(ii) processing, with the LSTM neural network, data representing the word that was selected for the output sequence at the preceding position in the output order to generate respective word scores for words in a pre-defined set of possible words, and
(iii) selecting, from the pre-defined set of possible words and based on the respective word scores, a particular word for the output sequence for the current position in the output order.

2. The one or more computer-storage media of claim 1 , wherein processing the alternative representation for the input image further comprises, for the initial position in the output order:
processing, with the LSTM neural network, a special start word to generate respective word scores for words in the pre-defined set of possible words; and
selecting, from the pre-defined set of possible words and based on the respective word scores, a particular word for the initial position in the output order of the output sequence of words.

3. The one or more computer-storage media of claim 1 , wherein processing the alternative representation for the input image further comprises:
using a left-to-right beam search decoding to generate a plurality of possible sequences and a respective sequence score for each of the possible sequences; and
selecting one or more highest-scoring possible sequences as descriptions of the input image.

4. The one or more computer-storage media of claim 1 , wherein the first neural network and the LSTM neural network are jointly trained.

5. The one or more computer-storage media of claim 1 , wherein the first neural network is a deep convolutional neural network.

6. The one or more computer-storage media of claim 5 , wherein:
the deep convolutional neural network comprises a plurality of core neural network layers each having a respective set of parameters;
processing the input image using the first neural network comprises processing the input image through each of the core neural network layers of the deep convolutional neural network; and
the alternative representation for the input image is the output generated by a last core neural network layer in the plurality of core neural network layers.

7. The one or more computer-storage media of claim 6 , wherein:
current values of the respective sets of parameters are determined by training a third neural network on a plurality of training images; and
the third neural network includes the plurality of core neural network layers and an output layer configured to, for each training image, receive the output generated by the last core neural network layer for the training image and generate a respective score for each of a plurality of object categories, the respective score for each of the plurality of object categories representing a predicted likelihood that the training image contains an image of an object from the object category.

8. The one or more computer-storage media of claim 1 , wherein the pre-defined set of possible words includes a vocabulary of words in the target natural language and a special stop word.

9. A computer-implemented method, comprising:
obtaining an input image;
processing the input image using a first neural network to generate an alternative representation for the input image; and
processing, with a long-short term memory (LSTM) neural network, the alternative representation for the input image to generate an output sequence of words in a target natural language that describes the input image, wherein:
the words in the output sequence are arranged according to an output order, and
processing the alternative representation for the input image comprises, for each position in the output order after an initial position:
(i) identifying a word that was selected for the output sequence at a preceding position in the output order that precedes the current position;
(ii) processing, with the LSTM neural network, data representing the word that was selected for the output sequence at the preceding position in the output order to generate respective word scores for words in a pre-defined set of possible words, and
(iii) selecting, from the pre-defined set of possible words and based on the respective word scores, a particular word for the output sequence for the current position in the output order.

10. The computer-implemented method of claim 9 , wherein processing the alternative representation for the input image further comprises, for the initial position in the output order:
processing, with the LSTM neural network, a special start word to generate respective word scores for words in the pre-defined set of possible words; and
selecting, from the pre-defined set of possible words and based on the respective word scores, a particular word for the initial position in the output order of the output sequence of words.

11. The computer-implemented method of claim 9 , wherein processing the alternative representation for the input image further comprises:
using a left-to-right beam search decoding to generate a plurality of possible sequences and a respective sequence score for each of the possible sequences; and
selecting one or more highest-scoring possible sequences as descriptions of the input image.

12. The computer-implemented method of claim 9 , wherein the first neural network and the LSTM neural network are jointly trained.

13. The computer-implemented method of claim 9 , wherein the first neural network is a deep convolutional neural network.

14. The computer-implemented method of claim 13 , wherein:
the deep convolutional neural network comprises a plurality of core neural network layers each having a respective set of parameters;
processing the input image using the first neural network comprises processing the input image through each of the core neural network layers of the deep convolutional neural network; and
the alternative representation for the input image is the output generated by a last core neural network layer in the plurality of core neural network layers.

15. The computer-implemented method of claim 14 , wherein:
current values of the respective sets of parameters are determined by training a third neural network on a plurality of training images; and
the third neural network includes the plurality of core neural network layers and an output layer configured to, for each training image, receive the output generated by the last core neural network layer for the training image and generate a respective score for each of a plurality of object categories, the respective score for each of the plurality of object categories representing a predicted likelihood that the training image contains an image

Assignees

• Google Llc

Inventors

• Bengio Samy
• Vinyals Oriol
• Toshev Alexander Toshkov
• Erhan Dumitru

Classifications

• G06N3/045 Primary

  Combinations of networks · CPC title

• G06F40/40

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title

• G06N3/0472 Primary

  Physics · mapped topic

• G06N3/0454

  Physics · mapped topic

• G06F17/28

  Physics · mapped topic